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6 changes: 4 additions & 2 deletions Programming-in-Python.md
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- [Scientific Python](https://github.com/Imperial-College-Data-Science-Society/Lecture-2-Scientific-Python)

## Courses

See **Python: Best practices** and **Python: Testing** under [Courses](./courses.md#course)

## Cheatsheets
- [Python Cheatsheet](https://www.pythoncheatsheet.org/)
- [PySheee: Python Cheatsheet](https://www.pythonsheets.com/)
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- [Python String Formatting Best Practices](https://realpython.com/python-string-formatting/)
- [The Best of the Best Practices (BOBP) Guide for Python](https://gist.github.com/sloria/7001839)
- [Dmitry Mugtasimov's Python software development practices](https://dmugtasimov-tech.blogspot.com/2016/12/my-python-software-development-practices.html)
- [Pluralsight: Python Best Practices for Code Quality](https://www.pluralsight.com/courses/python-best-practices-code-quality)
- [SO: Python coding standards/best practices](https://stackoverflow.com/questions/356161/python-coding-standards-best-practices)
- [Python Best Practices: 5 Tips For Better Code - Airbrake Blog](https://airbrake.io/blog/python/python-best-practices)
- [Python tutorial: Best practices and common mistakes to avoid](https://jaxenter.com/python-tutorial-best-practices-145959.html)
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- [Testing Python Applications with Pytest](https://semaphoreci.com/community/tutorials/testing-python-applications-with-pytest)
- [An Introduction to Mocking in Python](https://www.toptal.com/python/an-introduction-to-mocking-in-python)
- [PyCharm: Testing Your First Python Application](https://www.jetbrains.com/help/pycharm/testing-your-first-python-application.html)
- [Udemy Course: Automated Software Testing with Python](https://www.udemy.com/automated-software-testing-with-python/)
- [unittest — Unit testing framework](https://docs.python.org/2/library/unittest.html)

## Refactoring
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1 change: 1 addition & 0 deletions README.md
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- [Artificial Intelligence](README-details.md#artificial-intelligence)
- [Automation](README-details.md#automation)
- [Competitions](competitions.md)
- [Courses](courses.md)
- [Ethics / altruistic motives](README-details.md#ethics--altruistic-motives)
- [Java](./details/java-jvm.md#java)
- [Business / General / Semi-technical](./details/java-jvm.md#business--general--semi-technical)
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- Intel
- [Intel® Developer Zone](https://software.intel.com/en-us/home)
- [Intel® AI Developer Home Page](https://software.intel.com/en-us/ai)
- [Intel® AI Courses](https://software.intel.com/en-us/ai/courses)
- [Featured Course: AI from the Data Center to the Edge – An Optimized Path using Intel® Architecture](https://software.seek.intel.com/DataCenter_to_Edge_REG)
- [Intel® AI Developer Webinar Series](https://software.seek.intel.com/AIWebinarSeries?registration_source=IDZ) | [All webinars listing](https://intelvs.on24.com/vshow/IntelWebinarEvents/#content/2033414)
- The PlaidML Tensor Compiler - [webinar](https://event.on24.com/eventRegistration/console/EventConsoleApollo.jsp?&eventid=2026509&sessionid=1&username=&partnerref=&format=fhaudio&mobile=false&flashsupportedmobiledevice=false&helpcenter=false&key=B27628973F7FA8B9758983E373E36ED1&text_language_id=en&playerwidth=1000&playerheight=700&overwritelobby=y&eventuserid=246511746&contenttype=A&mediametricsessionid=207230377&mediametricid=2857349&usercd=246511746&mode=launch)
- nGraph - Unlocking next-generation performance with deep learning compilers: [webinar](https://intelvs.on24.com/vshow/IntelWebinarEvents/#content/2033414) | [slides](https://event.on24.com/event/20/33/41/2/rt/1/documents/resourceList1565185524584/s_ngraphwebinar1565185512750.pdf) | [homepage](https://www.ngraph.ai/) | [github](https://github.com/NervanaSystems/ngraph)
- Also see [Intel](../courses.md#intel) in [Courses](../courses.md#courses)

_Thanks to the great minds on the [mechanical sympathy](https://groups.google.com/forum/#!forum/mechanical-sympathy) mailing list for their responses to my queries on CPU probing._

## FPGA

- [Intel AI Developer Program - Deep Learning Inference With Intel® FPGAs](https://software.intel.com/en-us/ai/courses/deep-learning-inference-fpga)
- [Using FPGAs for Datacenter Acceleration](https://event.on24.com/eventRegistration/EventLobbyServlet?target=lobby20.jsp&eventid=2033432&sessionid=1&eventuserid=246511756&key=8678836B54A84876D7338D7BF7F87B88) | [Windows AI](https://docs.microsoft.com/en-us/windows/ai/) | [Intel® Distribution of OpenVINO™ Toolkit: Develop Multiplatform Computer Vision Solutions](https://software.intel.com/en-us/openvino-toolkit)
- Also see [FPGA](../courses.md#fpga) in [Courses](../courses.md#courses)

## GPU

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# Courses

## Algorithms

- [Algorithms at Coursera by Wayne and Sedgewick](https://www.coursera.org/course/algs4partI)

## Datacamp

- [Recommended Courses by Datacamp](https://www.datacamp.com/courses/)

## Dataiku

- [Dataiku Teachable](http://dataiku.teachable.com/courses)

## Data Science

- [Data Science Primer](https://elitedatascience.com/primer)
- [Coursera course: Getting and Cleaning Data](https://www.coursera.org/learn/data-cleaning?recoOrder=20&utm_medium=email&utm_source=recommendations&utm_campaign=u0faoCsqEemEkbug8nMVQQ)
- [Data Science courses on Coursera](https://www.coursera.org/learn/competitive-data-science)
- [Data courses on Udemy](https://www.udemy.com/courses/search/?ref=home&src=ukw&q=data)
- [Data courses on Udacity](https://eu.udacity.com/courses/school-of-data-science)
- [Latest Machine learning, visualization, data mining techniques. Online Master’s in Data Analytic from Penn State](https://twitter.com/analyticbridge/status/1102667686302179336)
- [Coursera Course: Probability and distribution](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8) [deadlink]
- [Coursera Data Science Methodology course](https://www.coursera.org/learn/data-science-methodology?aid=true)
- From Problem to Approach and From Requirements to Collection
- Business Understanding
- Analytic Approach
- Data Requirements
- Data Collection
- From Understanding to Preparation and From Modeling to Evaluation
- Data Understanding
- Data Preparation
- Modeling
- Model Evaluation

## Computer Vision

- [Introduction to Computer Vision, Udacity, GeorgiaTech](https://www.udacity.com/course/introduction-to-computer-vision--ud810) (free, paid for certification)
- [Stanford Computer Vision Lab : Teaching](http://vision.stanford.edu/teaching.html) - Contains publications other than courses (free)
- [Introduction to CV, IBM](https://www.coursera.org/learn/introduction-computer-vision-watson-opencv) (free, paid for certification)
- [Convolutional Neural Networks, Coursera](https://www.coursera.org/learn/convolutional-neural-networks) (free, paid for certification)

### Image Processing

- [Image and Video Processing course by Duke University, Coursera](https://www.coursera.org/learn/image-processing) (free, paid for certification)

## Fast.ai

- [Practical Deep Learning for Coders, v3](https://course.fast.ai/)
- [Part 2: Deep Learning from the Foundations](https://course.fast.ai/part2)
- [Introduction to Machine Learning for Coders](http://course18.fast.ai/ml)
- [Computational Linear Algebra](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md)
- [Code-First Introduction to Natural Language Processing](https://www.fast.ai/2019/07/08/fastai-nlp/)

## Intel

- [Intel® AI Courses](https://software.intel.com/en-us/ai/courses)
- [Featured Course: AI from the Data Center to the Edge – An Optimized Path using Intel® Architecture](https://software.seek.intel.com/DataCenter_to_Edge_REG)

### FPGA

- [Intel AI Developer Program - Deep Learning Inference With Intel® FPGAs](https://software.intel.com/en-us/ai/courses/deep-learning-inference-fpga)

## Machine Learning

- ML course by [Weights & Biases | WandB](https://wandb.com)
- [Code from the class](https://github.com/lukas/ml-class)
- [Setup Instructions](https://github.com/lukas/ml-class)
- [Slides](https://storage.googleapis.com/wandb/Bloomberg%20Class%201.pdf)
- [Building and Debugging CNNs](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE9193U0/bloomberg_class_2.pdf)
- [Introduction to ML](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE3Q7NN7/bloomberg_class_3.pdf)
- [Course material by Students of AI (Imperial College, London)](https://github.com/Students-for-AI/The-Academy-of-AI)
- [Comprehensive list of machine learning videos by Yaz](https://github.com/yazdotai/machine-learning-video-courses)

### Java/JVM
- [ML for Java Developers Course](http://numahub.com/courses/machine-learning-java-developers)

### Deep Learning

- [Code examples for the Stanford's course: TensorFlow for Deep Learning Research](https://github.com/chiphuyen/stanford-tensorflow-tutorials)

#### Reinforcement Learning

- Reinforcement Learning Crash Course by Central London Data Science meetup - [GitHub repo](https://github.com/central-ldn-data-sci/CrashCourseRL) | [Slides](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/Crash%20Course%20in%20Reinforcement%20Learning.pdf) | Notebooks: [1](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/CrashCourseRL.ipynb) | [2](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/crash_course_reinforcement_learning.ipynb) | [3](https://www.kaggle.com/blairyoung/crash-course-in-reinforcement-learning)

## Natural Language Processing (NLP)

- [How to Get Started with Deep Learning for Natural Language Processing (7-Day Mini-Course)](https://machinelearningmastery.com/crash-course-deep-learning-natural-language-processing/)

## Python: Best practices

- [Pluralsight: Python Best Practices for Code Quality](https://www.pluralsight.com/courses/python-best-practices-code-quality)

## Python: Testing

- [Udemy Course: Automated Software Testing with Python](https://www.udemy.com/automated-software-testing-with-python/)

## Statistics

- Statistics courses at [Coursera](https://www.coursera.org/courses?query=statistics&)
- [Udemy](https://www.udemy.com/courses/search/?src=ukw&q=statistics)
- [Udacity](https://eu.udacity.com/courses/all) - search for `Statistics`
- Harvard University: [Statistics 110](https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) | [more videos on their YouTube channel](https://www.youtube.com/user/Harvard/search?query=statistics)
- [Stanford University](https://online.stanford.edu/courses?keywords=statistics)
- [Statistical Inference [course]](https://www.coursera.org/learn/statistical-inference)

## Misc

- [Check out 50 most popular massive open online courses](https://www.onlinecoursereport.com/the-50-most-popular-moocs-of-all-time/) ([Tweet](https://twitter.com/java/status/984844161969983489))


# Contributing

Contributions are very welcome, please share back with the wider community (and get credited for it)!

Please have a look at the [CONTRIBUTING](CONTRIBUTING.md) guidelines, also have a read about our [licensing](LICENSE.md) policy.

---

Back to [main page (table of contents)](README.md)
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- Example: [how Dataiku DSS can be run on GraalVM for performance benefits](../examples/data/dataiku#dataiku-data-science-studio-dss) | [folder](../examples/data/dataiku)
- Additional useful resources for learning and exploration
- [User Enablement resources](https://pages.dataiku.com/dataiku-dss-user-enablement)
- [Dataiku Teachable](http://dataiku.teachable.com/courses)
- See [Dataiku](../courses.md#dataiku) under [Courses](../courses.md#course)
- [Dataiku YouTube Channel](https://www.youtube.com/channel/UCSMqVwPTmerMiCaL_zKRjBw)
- [Dataiku Academy](https://academy.dataiku.com/5.1/)
- [Dataiku Learn](https://www.dataiku.com/learn/) | [Tutorials](https://www.dataiku.com/learn/portals/tutorials.html) | [Dataiku ML: Your First Deep Learning Model](https://academy.dataiku.com/latest/tutorial/machine-learning/deep-learning-first.html) | [Dataiku: Machine Learning](https://academy.dataiku.com/latest/tutorial/machine-learning/skills.html)
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- Has a community forum and lots of resources on the internet, good feedback and posts on medium

Additional references

- https://course.fast.ai/
- See [fast.ai](../courses.md#fastai) under [Courses](../courses.md#course)
- https://docs.fast.ai/training.html
- https://forums.fast.ai/t/how-should-i-get-started-with-fast-ai-library/17627
- https://forums.fast.ai/t/another-treat-early-access-to-intro-to-machine-learning-videos/6826
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# Courses / books

- [Data Science Primer](https://elitedatascience.com/primer)
## Courses

- See [Courses](../courses.md#courses)

## Books

- [27 Amazing Data Science Books Every Data Scientist Should Read](https://www.analyticsvidhya.com/blog/2019/01/27-amazing-data-science-books-every-data-scientist-should-read/)
- [Coursera course: Getting and Cleaning Data](https://www.coursera.org/learn/data-cleaning?recoOrder=20&utm_medium=email&utm_source=recommendations&utm_campaign=u0faoCsqEemEkbug8nMVQQ)
- [Data Science courses on Coursera](https://www.coursera.org/learn/competitive-data-science)
- [Data courses on Udemy](https://www.udemy.com/courses/search/?ref=home&src=ukw&q=data)
- [Data courses on Udacity](https://eu.udacity.com/courses/school-of-data-science)
- [Latest Machine learning, visualization, data mining techniques. Online Master’s in Data Analytic from Penn State](https://twitter.com/analyticbridge/status/1102667686302179336)
- [Data Science Handbook](https://github.com/RishiSankineni/Data-Science-Swag/blob/master/The%20Data%20Science%20Handbook.pdf)
- [Coursera Course: Probability and distribution](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8) [deadlink]


# Contributing

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- [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)
- [Google Research: A large-scale dataset of manually annotated audio events](https://research.google.com/audioset/index.html)

## Courses

See [Courses](../courses.md#courses)

# Contributing

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- [The KDD process for extracting useful knowledge from volumes of data](http://shawndra.pbworks.com/f/The%20KDD%20process%20for%20extracting%20useful%20knowledge%20from%20volumes%20of%20data.pdf)
- [Data Mining: Practical ML Tools and Techniques by Witten, Frank and Mark 3rd edition](https://www.wi.hs-wismar.de/~cleve/vorl/projects/dm/ss13/HierarClustern/Literatur/WittenFrank-DM-3rd.pdf)
- [Foundational Methodology for Data Science - IBM Analytics Whitepaper](https://tdwi.org/~/media/64511A895D86457E964174EDC5C4C7B1.PDF)
- [Coursera Data Science Methodology course](https://www.coursera.org/learn/data-science-methodology?aid=true)
- From Problem to Approach and From Requirements to Collection
- Business Understanding
- Analytic Approach
- Data Requirements
- Data Collection
- From Understanding to Preparation and From Modeling to Evaluation
- Data Understanding
- Data Preparation
- Modeling
- Model Evaluation


- See [Courses](../courses.md#courses)

# Contributing

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- [Understanding statistical inference [video]](https://www.youtube.com/watch?v=tFRXsngz4UQ)
- [Four ideas of Statistical Inference](http://www.bristol.ac.uk/medical-school/media/rms/red/4_ideas_of_statistical_inference.html)
- [An Introduction to Statistical Learning [book]](http://www-bcf.usc.edu/~gareth/ISL/)
- [Statistical Inference [course]](https://www.coursera.org/learn/statistical-inference)
- See [Statistics courses](../courses.md#statistics) under [Courses](../courses.md#courses)
- [Understand Your Machine Learning Data With Descriptive Statistics in Python](https://machinelearningmastery.com/understand-machine-learning-data-descriptive-statistics-python/)
- [How to Use Statistics to Identify Outliers in Data](https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/)
- [Applying Physics functions](../presentations/data/Trackener-physics-functions-usage-example.pptx)
- [Chapter 2 of Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
- [Naked statistics](http://www.nakedstatistics.com/) | [Book on Amazon](https://www.amazon.com/Naked-Statistics-Stripping-Dread-Data/dp/1480590185) [Naked statistics flash cards](https://quizlet.com/90835490/naked-statistics-flash-cards/) | [Summary by Daniel Miessler](https://danielmiessler.com/projects/reading/summary-naked-statistics/)
- [Cartoon Guide to Statistics (Cartoon Guide Series)](https://www.amazon.co.uk/Cartoon-Guide-Statistics/dp/0062731025/ref=sr_1_1?hvadid=80814136501810&hvbmt=bb&hvdev=c&hvqmt=b&keywords=cartoon+guide+statistics&qid=1556047351&s=gateway&sr=8-1)
- [Journal of Statistical Software - TidyData](https://www.jstatsoft.org/article/view/v059i10/)
- Statistics courses at [Coursera](https://www.coursera.org/courses?query=statistics&) | [Udemy](https://www.udemy.com/courses/search/?src=ukw&q=statistics) | [Udacity](https://eu.udacity.com/courses/all) - search for `Statistics` | Harvard University: [Statistics 110](https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) | [more videos on their YouTube channel](https://www.youtube.com/user/Harvard/search?query=statistics) | [Stanford University](https://online.stanford.edu/courses?keywords=statistics)
- See [Statistics courses](../courses.md#statistics) under [Courses](../courses.md#courses)
- [15 Statistical Hypothesis Tests in Python (Cheat Sheet)](https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/?fbclid=IwAR102PXBzIdx8g8zejg9ssE7at8jrnyfAtiT95Rp8flo98p8qEFBho5HOG0)
- [Statistics cheatsheet by Nabih Ibrahim Bawazir](https://media.licdn.com/dms/document/C511FAQF31AWGmSTzMQ/feedshare-document-pdf-analyzed/0?e=1573030800&v=beta&t=11ugKu44wK--uA9WG98V_r6_LY_xu6I8Y-YSaM1BOsQ)
- [Statistics by Chris Albon](https://chrisalbon.com/#statistics) - covering Frequentist topics
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- [Papers and code](https://paperswithcode.com)
- [Awesome DL papers](https://github.com/terryum/awesome-deep-learning-papers)
- [List of articles related to deep learning applied to music](https://github.com/ybayle/awesome-deep-learning-music)
- [Course material by Students of AI (Imperial College, London)](https://github.com/Students-for-AI/The-Academy-of-AI)
- [Data.world's open data - catalog your data, wake up your hidden data workforce, and build a data-driven culture—faster](https://data.world/)
- [Browse state-of-the-art](https://paperswithcode.com/sota)
- [ML/DL/Data Science resources (scattered across the page)](https://github.com/ayonroy2000/100DaysOfML_TelegramGroup/blob/master/Resources.md)
- [Papers by Google X](../papers/google-x/README.md#papers-by-members-of-google-and-google-x-aka-x-team)
- ML course by [Weights & Biases | WandB](https://wandb.com)
- [Code from the class](https://github.com/lukas/ml-class)
- [Setup Instructions](https://github.com/lukas/ml-class)
- [Slides](https://storage.googleapis.com/wandb/Bloomberg%20Class%201.pdf)
- [Building and Debugging CNNs](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE9193U0/bloomberg_class_2.pdf)
- [Introduction to ML](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE3Q7NN7/bloomberg_class_3.pdf)
- See [courses](../courses.md)

# Contributing

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